import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def 							UpperCAmelCase		(       lowercase				,     lowercase=0.999				,     lowercase="cosine"				,     ):







				"""simple docstring"""



				if alpha_transform_type == "cosine":

								def alpha_bar_fn(lowercase				):
												return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(lowercase				):
												return math.exp(t * -12.0				)

				else:
								raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"				)

				__lowercase					     =				[]
				for i in range(lowercase				):
								__lowercase					     =				i / num_diffusion_timesteps
								__lowercase					     =				(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(lowercase				) / alpha_bar_fn(lowercase				)				,     lowercase				)				)
				return torch.tensor(lowercase				,     dtype=torch.floataa				)




class   _UpperCamelCase (		_UpperCAmelCase		,_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					List[str]     					=		[e.name for e in KarrasDiffusionSchedulers]
			__a    :					List[str]     					=		2





			@register_to_config
			def __init__(  self ,  lowerCAmelCase__ = 10_00 ,  lowerCAmelCase__ = 0.0_0085 ,  lowerCAmelCase__ = 0.012 ,  lowerCAmelCase__ = "linear" ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = "epsilon" ,  lowerCAmelCase__ = False ,  lowerCAmelCase__ = False ,  lowerCAmelCase__ = 1.0 ,  lowerCAmelCase__ = "linspace" ,  lowerCAmelCase__ = 0 ,  )	->   Optional[Any]:

							'''simple docstring'''


							if trained_betas is not None:
											__lowercase					     =				torch.tensor(lowerCAmelCase__ ,  dtype=torch.floataa		)
							elif beta_schedule == "linear":
											__lowercase					     =				torch.linspace(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  dtype=torch.floataa		)
							elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											__lowercase					     =				(
											    torch.linspace(beta_start**0.5 ,  beta_end**0.5 ,  lowerCAmelCase__ ,  dtype=torch.floataa		) ** 2
											)
							elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											__lowercase					     =				betas_for_alpha_bar(lowerCAmelCase__ ,  alpha_transform_type='''cosine'''		)
							elif beta_schedule == "exp":
											__lowercase					     =				betas_for_alpha_bar(lowerCAmelCase__ ,  alpha_transform_type='''exp'''		)
							else:
											raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"		)

							__lowercase					     =				1.0 - self.betas
							__lowercase					     =				torch.cumprod(self.alphas ,  dim=0		)

							#  set all values
							self.set_timesteps(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				use_karras_sigmas





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__=None		)	->   List[str]:

							'''simple docstring'''


							if schedule_timesteps is None:
											__lowercase					     =				self.timesteps

							__lowercase					     =				(schedule_timesteps == timestep).nonzero()

							# The sigma index that is taken for the **very** first `step`
							# is always the second index (or the last index if there is only 1)
							# This way we can ensure we don't accidentally skip a sigma in
							# case we start in the middle of the denoising schedule (e.g. for image-to-image)
							if len(self._index_counter		) == 0:
											__lowercase					     =				1 if len(lowerCAmelCase__		) > 1 else 0
							else:
											__lowercase					     =				timestep.cpu().item() if torch.is_tensor(lowerCAmelCase__		) else timestep
											__lowercase					     =				self._index_counter[timestep_int]

							return indices[pos].item()





			@property
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[Any]:

							'''simple docstring'''


							if self.config.timestep_spacing in ["linspace", "trailing"]:
											return self.sigmas.max()

							return (self.sigmas.max() ** 2 + 1) ** 0.5





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  )	->   torch.FloatTensor:

							'''simple docstring'''


							__lowercase					     =				self.index_for_timestep(lowerCAmelCase__		)

							__lowercase					     =				self.sigmas[step_index]
							__lowercase					     =				sample / ((sigma**2 + 1) ** 0.5)
							return sample





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = None ,  )	->   List[Any]:

							'''simple docstring'''


							__lowercase					     =				num_inference_steps

							__lowercase					     =				num_train_timesteps or self.config.num_train_timesteps

							# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
							if self.config.timestep_spacing == "linspace":
											__lowercase					     =				np.linspace(0 ,  num_train_timesteps - 1 ,  lowerCAmelCase__ ,  dtype=lowerCAmelCase__		)[::-1].copy()
							elif self.config.timestep_spacing == "leading":
											__lowercase					     =				num_train_timesteps // self.num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# casting to int to avoid issues when num_inference_step is power of 3
											__lowercase					     =				(np.arange(0 ,  lowerCAmelCase__		) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase__		)
											timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
											__lowercase					     =				num_train_timesteps / self.num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# casting to int to avoid issues when num_inference_step is power of 3
											__lowercase					     =				(np.arange(lowerCAmelCase__ ,  0 ,  -step_ratio		)).round().copy().astype(lowerCAmelCase__		)
											timesteps -= 1
							else:
											raise ValueError(
											    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."		)

							__lowercase					     =				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
							__lowercase					     =				np.log(lowerCAmelCase__		)
							__lowercase					     =				np.interp(lowerCAmelCase__ ,  np.arange(0 ,  len(lowerCAmelCase__		)		) ,  lowerCAmelCase__		)

							if self.config.use_karras_sigmas:
											__lowercase					     =				self._convert_to_karras(in_sigmas=lowerCAmelCase__ ,  num_inference_steps=self.num_inference_steps		)
											__lowercase					     =				np.array([self._sigma_to_t(lowerCAmelCase__ ,  lowerCAmelCase__		) for sigma in sigmas]		)

							__lowercase					     =				np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
							__lowercase					     =				torch.from_numpy(lowerCAmelCase__		).to(device=lowerCAmelCase__		)
							__lowercase					     =				torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2		), sigmas[-1:]]		)

							__lowercase					     =				torch.from_numpy(lowerCAmelCase__		)
							__lowercase					     =				torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2		)]		)

							if str(lowerCAmelCase__		).startswith('''mps'''		):
											# mps does not support float64
											__lowercase					     =				timesteps.to(lowerCAmelCase__ ,  dtype=torch.floataa		)
							else:
											__lowercase					     =				timesteps.to(device=lowerCAmelCase__		)

							# empty dt and derivative
							__lowercase					     =				None
							__lowercase					     =				None

							# for exp beta schedules, such as the one for `pipeline_shap_e.py`
							# we need an index counter
							__lowercase					     =				defaultdict(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   List[str]:

							'''simple docstring'''


							__lowercase					     =				np.log(lowerCAmelCase__		)

							# get distribution
							__lowercase					     =				log_sigma - log_sigmas[:, np.newaxis]

							# get sigmas range
							__lowercase					     =				np.cumsum((dists >= 0) ,  axis=0		).argmax(axis=0		).clip(max=log_sigmas.shape[0] - 2		)
							__lowercase					     =				low_idx + 1

							__lowercase					     =				log_sigmas[low_idx]
							__lowercase					     =				log_sigmas[high_idx]

							# interpolate sigmas
							__lowercase					     =				(low - log_sigma) / (low - high)
							__lowercase					     =				np.clip(lowerCAmelCase__ ,  0 ,  1		)

							# transform interpolation to time range
							__lowercase					     =				(1 - w) * low_idx + w * high_idx
							__lowercase					     =				t.reshape(sigma.shape		)
							return t





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   torch.FloatTensor:

							'''simple docstring'''


							__lowercase					     =				in_sigmas[-1].item()
							__lowercase					     =				in_sigmas[0].item()

							__lowercase					     =				7.0  # 7.0 is the value used in the paper
							__lowercase					     =				np.linspace(0 ,  1 ,  lowerCAmelCase__		)
							__lowercase					     =				sigma_min ** (1 / rho)
							__lowercase					     =				sigma_max ** (1 / rho)
							__lowercase					     =				(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
							return sigmas





			@property
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Dict:

							'''simple docstring'''


							return self.dt is None





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ = True ,  )	->   Union[SchedulerOutput, Tuple]:

							'''simple docstring'''


							__lowercase					     =				self.index_for_timestep(lowerCAmelCase__		)

							# advance index counter by 1
							__lowercase					     =				timestep.cpu().item() if torch.is_tensor(lowerCAmelCase__		) else timestep
							self._index_counter[timestep_int] += 1

							if self.state_in_first_order:
											__lowercase					     =				self.sigmas[step_index]
											__lowercase					     =				self.sigmas[step_index + 1]
							else:
											# 2nd order / Heun's method
											__lowercase					     =				self.sigmas[step_index - 1]
											__lowercase					     =				self.sigmas[step_index]

							# currently only gamma=0 is supported. This usually works best anyways.
							# We can support gamma in the future but then need to scale the timestep before
							# passing it to the model which requires a change in API
							__lowercase					     =				0
							__lowercase					     =				sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
											__lowercase					     =				sigma_hat if self.state_in_first_order else sigma_next
											__lowercase					     =				sample - sigma_input * model_output
							elif self.config.prediction_type == "v_prediction":
											__lowercase					     =				sigma_hat if self.state_in_first_order else sigma_next
											__lowercase					     =				model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
											    sample / (sigma_input**2 + 1)
											)
							elif self.config.prediction_type == "sample":
											__lowercase					     =				model_output
							else:
											raise ValueError(
											    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"		)

							if self.config.clip_sample:
											__lowercase					     =				pred_original_sample.clamp(
											    -self.config.clip_sample_range ,  self.config.clip_sample_range		)

							if self.state_in_first_order:
											# 2. Convert to an ODE derivative for 1st order
											__lowercase					     =				(sample - pred_original_sample) / sigma_hat
											# 3. delta timestep
											__lowercase					     =				sigma_next - sigma_hat

											# store for 2nd order step
											__lowercase					     =				derivative
											__lowercase					     =				dt
											__lowercase					     =				sample
							else:
											# 2. 2nd order / Heun's method
											__lowercase					     =				(sample - pred_original_sample) / sigma_next
											__lowercase					     =				(self.prev_derivative + derivative) / 2

											# 3. take prev timestep & sample
											__lowercase					     =				self.dt
											__lowercase					     =				self.sample

											# free dt and derivative
											# Note, this puts the scheduler in "first order mode"
											__lowercase					     =				None
											__lowercase					     =				None
											__lowercase					     =				None

							__lowercase					     =				sample + derivative * dt

							if not return_dict:
											return (prev_sample,)

							return SchedulerOutput(prev_sample=lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  )	->   torch.FloatTensor:

							'''simple docstring'''


							__lowercase					     =				self.sigmas.to(device=original_samples.device ,  dtype=original_samples.dtype		)
							if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase__		):
											# mps does not support float64
											__lowercase					     =				self.timesteps.to(original_samples.device ,  dtype=torch.floataa		)
											__lowercase					     =				timesteps.to(original_samples.device ,  dtype=torch.floataa		)
							else:
											__lowercase					     =				self.timesteps.to(original_samples.device		)
											__lowercase					     =				timesteps.to(original_samples.device		)

							__lowercase					     =				[self.index_for_timestep(lowerCAmelCase__ ,  lowerCAmelCase__		) for t in timesteps]

							__lowercase					     =				sigmas[step_indices].flatten()
							while len(sigma.shape		) < len(original_samples.shape		):
											__lowercase					     =				sigma.unsqueeze(-1		)

							__lowercase					     =				original_samples + noise * sigma
							return noisy_samples





			def __len__(  self		)	->   Any:

							'''simple docstring'''


							return self.config.num_train_timesteps
import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
		print("""Googling.....""")
		__a    :	Tuple         =    """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
		__a    :	List[str]         =    requests.get(url, headers={"""UserAgent""": UserAgent().random})
		# res.raise_for_status()
		with open("""project1a.html""", """wb""") as out_file:  # only for knowing the class
				for data in res.iter_content(1_0_0_0_0):
						out_file.write(data)
		__a    :	Dict         =    BeautifulSoup(res.text, """html.parser""")
		__a    :	Any         =    list(soup.select(""".eZt8xd"""))[:5]

		print(len(links))
		for link in links:
				if link.text == "Maps":
						webbrowser.open(link.get("""href"""))
				else:
						webbrowser.open(F'''https://google.com{link.get("href")}''')